import cv2 import numpy as np from registry import registry @registry.register("Original") def original(image): return image @registry.register("Grayscale") def grayscale(image): return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) @registry.register("Gaussian Blur", defaults={'kernel_size': 15}, min_vals={'kernel_size': 3}, max_vals={'kernel_size': 31}, step_vals={'kernel_size': 2}) def gaussian_blur(image, kernel_size: int = 15): return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0) @registry.register("Pencil Sketch") def pencil_sketch(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) inverted = cv2.bitwise_not(gray) blurred = cv2.GaussianBlur(inverted, (21, 21), 0) inverted_blurred = cv2.bitwise_not(blurred) return cv2.divide(gray, inverted_blurred, scale=256.0) @registry.register("Sepia") def sepia(image): kernel = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131] ]) return cv2.transform(image, kernel) @registry.register("Edge Enhance", defaults={'intensity': 1.5}, min_vals={'intensity': 0.5}, max_vals={'intensity': 5.0}, step_vals={'intensity': 0.1}) def edge_enhance(image, intensity: float = 1.5): kernel = np.array([ [-1 * intensity, -1 * intensity, -1 * intensity], [-1 * intensity, 9 * intensity, -1 * intensity], [-1 * intensity, -1 * intensity, -1 * intensity] ]) return cv2.filter2D(image, -1, kernel) @registry.register("Canny Edge", defaults={'lower_threshold': 100, 'upper_threshold': 200, 'convert_to_gray': True}, min_vals={'lower_threshold': 0, 'upper_threshold': 255}, max_vals={'lower_threshold': 255, 'upper_threshold': 255}, step_vals={'lower_threshold': 1, 'upper_threshold': 1}) def canny_edge(image, lower_threshold=100, upper_threshold=200, convert_to_gray=True): if convert_to_gray and len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image edges = cv2.Canny(gray, lower_threshold, upper_threshold) return edges